微地震信号到时自动拾取方法

本文讨论了用于微地震信号到时自动拾取的几种方法的原理及特点,包括长短时均值比(STA/LTA)方法、AIC方法、基于高阶统计量偏斜度和峰度的PAI-S/K方法等,提出了移动时窗峰度的快速算法和改进的峰度拾取初至方法.对我国西部某地观测到的13359个微地震记录,采用两种时窗进行了初至到时拾取,并与人工拾取的结果进行了对比.为使所研究的方法达到最佳效果,采用DE全局搜索方法,以人工拾取的初至作为参照,以时差在0.3 s以内的记录所占百分比作为目标函数,自动搜索最佳的拾取参数.结果显示,在拾取时窗选为P波初至前3 s至S波初至位置时,AIC方法的结果最佳,时差在0.3 s以内的记录占比达到93.6%;在拾取时窗选为包含S波到时的时窗时,改进的峰度法效果最佳,时差在0.3 s以内的记录占比83.8%.     

STA/LTA—AIC算法对地震P波震相拾取稳定性影响

选取区域地震台网记录的地震波形数据,使用STA/LTA算法与STA/LTA—AIC算法,进行地震P波震相初至到时自动拾取,对地方震及震中距较大的震相进行P波震相拾取效果分析,发现:STA/LTA算法对于地方震P波震相识别精度较高,与STA/LTA—AIC算法拾取的P波震相初至到时相差不大;震中距变大后,STA/LTA算法对P波拾取位置相对于最佳位置向后延迟,STA/LTA—AIC算法有效矫正了STA/LTA算法拾取位置的延迟问题,与人工拾取位置差别可忽略不计。     

一种适用于地方震事件的S波到时自动拾取方法

基于特征值分解方法,本文讨论了一种适用于地方震事件S波震相到时拾取的自动处理算法.该算法计算参数少、简便快捷、易于实现,通过选用七个不同长度的时间窗,有效地减小了窗长选择不合理所引起的震相拾取误差.利用福建地震台网记录的9855条三分向波形记录进行测试,结果表明:本文方法的S波平均拾取偏差为(0.003±1.34)s,...     

利用标准时频变换方法在强噪声环境下无偏拾取地震P波、S波到时

地震P波、S波到时是精确分析地震水平位置、深度与速度结构等的重要参数,如何准确拾取P波和S波到时是地震学的一项重要的基础工作.大数据量与强噪声环境给地震到时的自动拾取带来了很大挑战.在频率域中可将信号与噪声分离,但会造成震相的偏移.针对上述问题,本文在STA/LTA、AIC方法的基础上,引入了标准时频变换(Normal...     

一种基于PPNet的地震直达P波到时拾取方法

针对现有地震直达P波到时拾取网络精度低、误差大等问题,结合UNet++编码、解码器,融入特征过滤器设计一种具有地震震相特征分析与融合能力的轻量级P波到时拾取网络PPNet,实现对地震P波的高精度、低误差拾取。首先,该网络在编码器模块采用大卷积核、低通道数的卷积层,对输入的地震信号进行深度特征提取;其次,在解码器模块的特征还原过程中加入特征融合机制,补全特征信息,避免序列特征污染问题;最后,仅对编码器后三个下采样模块添加特征过滤器,深入挖掘特征序列,通过细化P波到时特征,提升到时拾取精度。实验结果表明,提出的网络在0.1 s、0.2 s、0.3 s误差阈值下P波拾取率分别为80.73%、94.01%、97.81%,平均绝对误差0.078 s,均方误差0.021,与现有P波拾取传统方法和深度学习算法相比性能更优。     

时窗能量特征法拾取微地震波初始到时的可行性研究

微地震（MS）波初始到时的自动拾取是MS监测数据处理的关键技术之一,也是实现MS震源自动定位的技术难点.本文在MS震源定位结果反演与推断的研究基础上,对不同类型MS波的到时点特征进行了分析与描述,并对不同时窗长度下能量特征值的变化规律进行了研究,提出了控制时窗移动范围和确定时窗长度自适应参数的具体方法,利用建立的MS波初始到时点特征的模式识别库,对拾取的到时进行模式归类、定量评价和匹配,提高了自动拾取结果的可靠性.研究结果表明,对典型的信噪比高的MS波,到时自动拾取的结果与手工拾取的结果基本一致;对无量纲大振幅的MS波,到时自动拾取结果的可靠性要高于手工拾取,对信噪比低和到时点不清晰的MS波自动拾取的可靠性较低.     

P波初至到时自动拾取技术研究

基于粘滞性单自由度振动器响应下的能量转换理论,提出利用阻尼能量作为目标函数的P波震相到时拾取方法——SDOF Picker算法。使用该方法对江苏及邻区2010—2016年实际记录的9 607组P波初至进行到时自动拾取测试,以地震编目中人工拾取到时为基准,与利用AIC算法自动抬取的结果进行了系统性对比分析,结果显示:SDOF Picker算法和AIC算法自动拾取P波初至的准确率分别为97.1%、91.8%,中值偏差分别为(0.02±0.61)s、(0.05±0.77)s,方差分别为0.37 s2、0.60 s2,这表明SDOF Picker算法的在准确率和拾取精度方面均优于AIC算法。     

基于支持向量机的地震体波震相自动识别及到时自动拾取

面对海量地震资料,自动准确地拾取震相并确定其到时的需求非常迫切.基于支持向量机技术,本文提出了使用两个分类器SSD和SPS自动识别地震体波震相并自动拾取其到时的方法.相比于传统的自动拾取方法,本文方法能够更准确地识别震相并区分P波和S波.进一步地,我们提出了利用台阵资料辅助识别震相的方案,有效地提高了地震震相拾取的准确率.     

基于深度学习卷积神经网络的地震波形自动分类与识别

发展高效、高精度、普适性强的自动波形拾取算法在地震大数据时代背景下显得越来越重要.波形自动拾取算法的主要挑战来自如何适应不同区域的不同类型地震事件的分类与筛选.本文针对地震事件-噪音分类这一问题,使用13839个汶川地震余震事件建立数据集,应用深度学习卷积神经网络(CNN)方法进行训练,并用8900个新的汶川余震事件作为检测数据集,其训练和检测准确率均达到95%以上.在对连续波形的检测中,CNN方法在精度和召回率上优于STA/LTA和Fbpicker传统方法,并能找出大量人工挑选极易遗漏的微震事件.最后,我们应用训练好的最优模型对选自全国台网的441个台站8天的连续波形数据进行了识别、到时挑取及与参考地震目录关联,CNN检出7016段波形,用自动挑选算法拾取到1380对P,S到时,并与540个地震目录事件成功关联,对1级以上事件总体识别准确率为54%,二级以上为80%,证明了CNN模型具有泛化能力,初步展示了CNN在发展兼具效率、精度、普适性算法,实时地震监测等应用上具有巨大潜力.     

基于深度卷积神经网络的地震震相拾取方法研究

地震震相拾取是地震数据自动处理的首要环节,包括了信号检测、到时估计和震相识别等过程,震相拾取的准确性直接影响到后续事件关联处理的性能,影响观测报告的质量.为了提高震相拾取的准确性,进而提高观测报告质量,本文采用深度卷积神经网络方法来解决震相拾取问题,构建了多任务卷积神经网络模型,设计了分类和回归的联合损失函数,定义了基于加权的分类损失函数,以三分量地震台站的波形数据作为输入,同时实现对震相的检测识别和到时的精确估计.利用美国南加州地震台网的200万条震相和噪声数据对模型进行训练、验证和测试,对于测试集中直达波P、S震相识别的查全率达到98%以上,到时估计的标准偏差分别为0.067s,0.082s.利用迁移学习和数据增强,将模型用于对我国东北地区台网的6个台站13000条数据的训练、验证和测试中,对该数据集P、S震相查全率分别达到91.21%、85.65%.基于迁移训练后的模型,设计了用于连续数据的震相拾取方法,利用连续的地震数据对该算法进行了实际应用测试,并与国家数据中心和中国地震局的观测报告进行比对,该方法的震相检测识别率平均可达84.5%,验证了该方法在实际应用中的有效性.本文所提出的方法展示了深度神经网络在地震震相拾取中的优异性能,为地震震相和事件的检测识别提供了新的思路.     

USTC-Pickers: a Unified Set of seismic phase pickers Transfer learned for China

Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China. To mitigate this problem, we build a unified set of customized seismic phase pickers for different levels of use in China. We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet. This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China. Then, using different subsets of the DiTing data, we fine-tune the base picker to better adapt to different regions. In total, we provide 5 pickers for major tectonic blocks in China, 33 pickers for provincial-level administrative regions, and 2 special pickers for the Capital area and the China Seismic Experimental Site. These pickers show improved performance in respective regions which they are customized for. They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets. We anticipate that this picker set will facilitate earthquake monitoring in China.     

Reliability of the automatic procedures for locating earthquakes in southwestern Alps and northern Apennines (Italy)

Reliable automatic procedure for locating earthquake in quasi-real time is strongly needed for seismic warning system, earthquake preparedness, and producing shaking maps. The reliability of an automatic location algorithm is influenced by several factors such as errors in picking seismic phases, network geometry, and velocity model uncertainties. The main purpose of this work is to investigate the performances of different automatic procedures to choose the most suitable one to be applied for the quasi-real-time earthquake locations in northwestern Italy. The reliability of two automatic-picking algorithms (one based on the Characteristic Function (CF) analysis, CF picker, and the other one based on the Akaike’s information criterion (AIC), AIC picker) and two location methods (“Hypoellipse” and “NonLinLoc” codes) is analysed by comparing the automatically determined hypocentral coordinates with reference ones. Reference locations are computed by the “Hypoellipse” code considering manually revised data and tested using quarry blasts. The comparison is made on a dataset composed by 575 seismic events for the period 2000–2007 as recorded by the Regional Seismic network of Northwestern Italy. For P phases, similar results, in terms of both amount of detected picks and magnitude of travel time differences with respect to manual picks, are obtained applying the AIC and the CF picker; on the contrary, for S phases, the AIC picker seems to provide a significant greater number of readings than the CF picker. Furthermore, the “NonLinLoc” software (applied to a 3D velocity model) is proved to be more reliable than the “Hypoellipse” code (applied to layered 1D velocity models), leading to more reliable automatic locations also when outliers (wrong picks) are present.     

Enhancing seismic P phase arrival picking based on wavelet denoising and kurtosis picker

P phase arrival picking of weak signals is still challenging in seismology. A wavelet denoising is proposed to enhance seismic P phase arrival picking, and the kurtosis picker is applied on the wavelet-denoised signal to identify P phase arrival. It has been called the WD-K picker. The WD-K picker, which is different from those traditional wavelet-based pickers on the basis of a single wavelet component or certain main wavelet components, takes full advantage of the reconstruction of main detail wavelet components and the approximate wavelet component. The proposed WD-K picker considers more wavelet components and presents a better P phase arrival feature. The WD-K picker has been evaluated on 500 micro-seismic signals recorded in the Chinese Yongshaba mine. The comparison between the WD-K pickings and manual pickings shows the good picking accuracy of the WD-K picker. Furthermore, the WD-K picking performance has been compared with the main detail wavelet component combining-based kurtosis (WDC-K) picker, the single wavelet component-based kurtosis (SW-K) picker, and certain main wavelet component-based maximum kurtosis (MMW-K) picker. The comparison has demonstrated that the WD-K picker has better picking accuracy than the other three-wavelet and kurtosis-based pickers, thus showing the enhanced ability of wavelet denoising.     

A robust method for microseismic event detection based on automatic phase pickers

We present a robust method for the automatic detection and picking of microseismic events that consists of two steps. The first step provides accurate single-trace picks using three automatic phase pickers adapted from earthquake seismology. In the second step, a multi-channel strategy is implemented to associate (or not) the previous picks with actual microseismic signals by taking into account their expected alignment in all the available channels, thus reducing the false positive rate. As a result, the method provides the number of declared microseismic events, a confidence indicator associated with each of them, and the corresponding traveltime picks. Results using two field noisy data records demonstrate that the automatic detection and picking of microseismic events can be carried out with a relatively high confidence level and accuracy.     

Improvements in mining induced microseismic source locations at the Lucky Friday mine using an automated whole-waveform analysis system

For years, severe rockburst problems at the Lucky Friday mine in northern Idaho have been a persistent safety hazard and an impediment to production. An MP250 based microseismic monitoring system, which uses simple voltage threshold picking of first arrivals, has been used in this mine since 1973 to provide source locations and energy estimates of seismic events. Recently, interest has been expressed in developing a whole waveform microseismic monitoring system for the mine to provide more accurate source locations and information about source characteristics. For this study, we have developed a prototype whole-waveform microseismic monitoring system based on a 80386 computer equipped with a 50 kHz analog-digital convertor board. The software developed includes a data collection program, a data analysis program, and an event detection program. Whole-waveform data collected and analyzed using this system during a three-day test have been employed to investigate sources of error in the hypocenter location process and to develop an automatic phase picker appropriate for microseismic events.Comparison of hypocenter estimates produced by the MP250 system to those produced by the whole-waveform system shows that significant timing errors are common in the MP250 system and that these errors caused a large part of the scatter evident in the daily activity plots produced at the mine. Simulations and analysis of blast data show that analytical control over the solutions is strongly influenced by the array geometry. Within the geophone array, large errors in the velocity model or moderate timing errors may result in small changes in the solution, but outside the array, the solution is very sensitive to small changes in the data.Our whole-waveform detection program picks event onset times and determines event durations by analysis of a segmented envelope function (SEF) derived from the microseismic signal. The detection program has been tested by comparing its arrival time picks to those generated by human analysis of the data set. The program picked 87% of the channels that were picked by hand with a standard error of 0.75 milliseconds. Source locations calculated using times provided by our entire waveform detection program were similar to those calculated using hand-picked arrival times. In particular, they show far less scatter than source locations calculated using arrival times based on simple voltage threshold picking of first arrivals.     

基于深度学习到时拾取自动构建长宁地震前震目录

将深度学习到时拾取、震相关联技术与传统定位方法联系起来,构建一套连续波形自动化处理与地震目录自动构建流程,对于高效充分利用地震资料,提升微震检测能力具有十分重要的意义.我们应用最新发展的迁移学习震相识别技术、震相自动关联技术,对长宁Ms6.0地震震中附近21个台站震前半个月(6月1日-6月17日)的连续记录波形进行P、...     

云南腾冲地区三维地震定位

本文利用中国数字测震台网和流动台站的地震资料,基于参数优化的AICD自动拾取算法和质量评估方案得到了高质量的震相到时,并在此基础上使用一维、三维定位方法对腾冲地区的799次地震事件进行了重新定位。定位结果显示:水平方向上,一维、三维重定位结果相差较小;深度方向上,三维定位的震源成丛分布比一维定位结果更加密集,地震主要位于地壳内低速层之上。分别利用一维、三维定位方法对典型地震、人工震源进行定位,结果表明,三维定位的精度明显优于一维定位,其在水平、深度方向上的平均绝对定位误差分别为0.7 km和1.3 km。      

东祁连山北缘断裂带基于深度学习的密集台阵地震事件快速检测与定位研究

为监测东祁连山北缘断裂带附近的地震活动性,布设包含240台短周期地震仪的面状密集台阵,进行约30 d的连续观测。首先使用基于深度学习的多台站地震事件检测算法（CNNDetector）进行地震事件检测,然后使用震相拾取网络（PhaseNet）对地震事件进行P波和S波到时拾取,其次使用震相关联算法（REAL）进行震相关联及初定位,最后使用双差定位（hypoDD）进行地震重定位,最终的精定位地震目录中共有517个地震。在密集台阵观测期间,中国地震台网正式地震目录中共有39个位于台阵内的地震事件,相比而言,密集台阵检测到大量小于0级的地震。因此通过布设密集台阵,可提高活动断裂微地震活动性的监测能力。与历史地震空间分布相比,密集台阵地震精定位分布具有较好的一致性,表现出更明显的线性分布特征。基于地震分布,发现研究区域存在与地表断层迹线走向不同的隐伏活跃断裂。     

基于U形卷积神经网络的震相识别与到时拾取方法研究

精确获取震相到时是地震定位和地震走时成像等研究的重要基础.近年来,随着地震台站的不断加密,地震台网监测到的地震数量成倍增长,发展快速、准确、适用性强的震相到时自动拾取算法是地震行业的迫切需求.本文在前人工作基础上,发展了Pg、Sg震相自动识别与到时拾取的U网络算法（Unet_cea）,使用汶川余震和首都圈地震台网记录的89344个不同震级、不同信噪比的样本进行训练和测试.研究表明,U网络能够较好地识别Pg、Sg震相类型和拾取到时,Pg、Sg震相的正确识别率分别为81%和79.1%,与人工标注到时的均方根误差分别为0.41 s和0.54 s.U网络在命中率、均方根误差等性能指标上均明显优于STA/LTA和峰度分析自动拾取方法.研究获得的最优模型可以为区域地震台网的自动处理提供辅助.     

Velocity anomalies: An alternative explanation based on data from laboratory experiments

Locations and velocities were calculated for microseisms occurring in samples of rock subjected to triaxial loading and injection of pore fluid. This was accomplished by analyzing arrival times of acoustic emission using an automatic first arrival picker. Apparent velocity anomalies were observed prior to both failure of intact samples and violent slip in samples containing saw cuts. Further analysis revealed that these fluctuations in calculated velocity were not due to changes in the true seismie velocity. Instead, variations in calculated velocity are shown to be related to sampling errors in picking first arrivals. The systematic picking of late first arrivals for small magnitude events was found to be a persistent bias resulting in low calculated velocities. This has encouraged the reexamination of earthquake records to determine how important sampling biases are in contributing to reported velocity anomalies.